Strong intelligence research leads data-driven decision while investing in early-stage company

Before we get to different methods, we set aside traditional discounted cash flow or other methods that rely on current financial metrics. Because many companies will be pre-revenue and these methods offer a conservative valuation without considering the potential for high growth.

Other qualitative methods rely on ongoing market trends or significant events that will materially affect growth and hence valuation. Questions like Are there any emerging trends that could impact the company’s future?, or Are there any industry-wide events that could have an impact on its sales and the market the startup is attacking?.

Some examples include changes in regulations, availability of UPI infrastructure in India or research progress in ML / AI or growth of ‘fulfilled by Amazon’.

Such events or trends, their early identification requires significant investment of time and effort to deeply understand a domain and translate them into investment theses. Even then portfolio returns from such insights is not guaranteed.

Therefore investors take solace in data to make better decisions. There are 3 main ways to use data in making early stage valuation decisions.

First method is common among early stage accelerators that take fixed ownership for a fixed investment amount. For example YCombinator in the US commits $125K for 7% + $375K as uncapped.

Investors may be fixated on ownership percentages for the amounts they commit. This method obviously does not work across all sectors and enforces some self-selection in the type of startups that target these programs/investors.

If the call rests with the founders on a future valuation for the amount invested (eg as used in SAFE agreements), data and transaction intelligence is required for that as well.

Second method is looking at valuation using multiples. For example, for early stage 1-3 X trailing GMV, or 10-25 X EBITDA or 5-10 X forward Revenue can be a basis for valuation. Of course these numbers vary by sectors and the revenue stage of the company.

You can also do such multiples based on operating metrics like revenue per monthly active user or API calls, engagement metrics at community/site/app level, distribution of the solution and then arrive at valuations accordingly.

Third method is looking at comparable deals in the recent past. Transactions in the market can be a useful guide when valuing a company. Data around company stage, dilution, investment amount, total amount raised by the company, age at deal, investor types can help determine comparable deals.

Another method that investors adopt is looking at business models that are being operationalized by the startup. For example returns from asset-heavy businesses versus asset-light businesses are going to be different and therefore investors may value them differently. Certain sectors have a long gestation period with no returns in sight for 3+ years, so investors taking such bets will incorporate these into the valuations they arrive at.

So availability of data for use in these different methods is critical. And such data has to cover across sectors/sub-sectors will help investors arrive at a range for valuation for their area of interest.

By using the trends, events, multiples, comparable transactions or business models you can effectively value an early stage company. Caveat being able to access and keep such data updated while your deal funnel keeps filling.

By understanding the factors that affect a geo, sector, company you can make an informed assessment of a startup’s potential value. Of course other considerations like team quality and their ability to solve tough problems together, worst/best case scenarios emerging, barriers to entry or duplication potential can be material as well.

(Disclaimer: The author is
, Founder, PrivateCircle.co.
Recommendations, suggestions, views and opinions given by the experts are their own. These do not represent the views of Economic Times)